Data Warehousing, Multi-Dimensional Data Models and OLAP

Data Warehousing, Multi-Dimensional Data Models and OLAP

Prasad M. Deshpande (IBM Almaden Research Center, USA) and Karthikeyan Ramasamy (Juniper Networks, USA)
DOI: 10.4018/978-1-59904-951-9.ch014
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Abstract

Since the advent of information technology, businesses have been collecting vast amounts of data about their daily transactions. For example, a company keeps track of data regarding the sales of its various products at different stores over a period of time. Businesses can gain valuable insights by analyzing this data to spot trends and correlations in the data. Data warehousing, multidimensional analysis, and online analytical processing (OLAP) refer to a set of technologies that address the problem of business data analysis. Data warehousing has become the established paradigm for knowledge workers to sift through mountains of historical data in order to extract nuggets of business information. Data analysis tools have been used in various forms historically since the 1960s (Pendse, 2003). Recently, there has been a rapid growth in the industry, with the total worldwide OLAP market estimated at about $3.7 billion in 2003 (Pendse). This has also been an active area of research with many contributions in data warehouse design, storage, view selection, cube computation, indexing, and query evaluation.

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